Causal Multi-Agent Reinforcement Learning: Review and Open Problems

• URL: http://arxiv.org/abs/2111.06721v1
• Date: Fri, 12 Nov 2021 13:44:31 GMT
• Title: Causal Multi-Agent Reinforcement Learning: Review and Open Problems
• Authors: St John Grimbly, Jonathan Shock, Arnu Pretorius
• Abstract summary: This paper serves to introduce the reader to the field of multi-agent reinforcement learning (MARL) We highlight key challenges in MARL and discuss these in the context of how causal methods may assist in tackling them.
• Score: 5.0519220616720295
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper serves to introduce the reader to the field of multi-agent reinforcement learning (MARL) and its intersection with methods from the study of causality. We highlight key challenges in MARL and discuss these in the context of how causal methods may assist in tackling them. We promote moving toward a 'causality first' perspective on MARL. Specifically, we argue that causality can offer improved safety, interpretability, and robustness, while also providing strong theoretical guarantees for emergent behaviour. We discuss potential solutions for common challenges, and use this context to motivate future research directions.

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